Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
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README.md

MobileNet-SSD

A caffe implementation of MobileNet-SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

Network mAP Download Download
MobileNet-SSD 72.7 train deploy

Run

  1. Download SSD source code and compile (follow the SSD README).
  2. Download the pretrained deploy weights from the link above.
  3. Put all the files in SSD_HOME/examples/
  4. Run demo.py to show the detection result.
  5. You can run merge_bn.py to generate a no bn model, it will be much faster.

Train your own dataset

  1. Convert your own dataset to lmdb database (follow the SSD README), and create symlinks to current directory.
ln -s PATH_TO_YOUR_TRAIN_LMDB trainval_lmdb
ln -s PATH_TO_YOUR_TEST_LMDB test_lmdb
  1. Create the labelmap.prototxt file and put it into current directory.
  2. Use gen_model.sh to generate your own training prototxt.
  3. Download the training weights from the link above, and run train.sh, after about 30000 iterations, the loss should be 1.5 - 2.5.
  4. Run test.sh to evaluate the result.
  5. Run merge_bn.py to generate your own no-bn caffemodel if necessary.
python merge_bn.py --model example/MobileNetSSD_deploy.prototxt --weights snapshot/mobilenet_iter_xxxxxx.caffemodel

About some details

There are 2 primary differences between this model and MobileNet-SSD on tensorflow:

  1. ReLU6 layer is replaced by ReLU.
  2. For the conv11_mbox_prior layer, the anchors is [(0.2, 1.0), (0.2, 2.0), (0.2, 0.5)] vs tensorflow's [(0.1, 1.0), (0.2, 2.0), (0.2, 0.5)].

Reproduce the result

I trained this model from a MobileNet classifier(caffemodel and prototxt) converted from tensorflow. I first trained the model on MS-COCO and then fine-tuned on VOC0712. Without MS-COCO pretraining, it can only get mAP=0.68.

Mobile Platform

You can run it on Android with my another project rscnn.